SynthesisSensors (Basel, Switzerland)2024
Machine Learning for Multimodal Mental Health Detection: A Systematic Review of Passive Sensing Approaches.
Synthesis in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
36 citing papers in PubMed, 1 synthesis or guideline pooled it, 101 citations in OpenAlex.
- Multimodal observable cues in mood, anxiety, and borderline personality disorders: a review of reviews to inform explainable AI in mental health.Frontiers in artificial intelligence · 2025Pooled it
- Reliability-Aware Cross-Modal Learning Behavior Sensing for Student Cognitive Bias Recognition and Teaching-Oriented Psychological Risk Warning.Sensors (Basel, Switzerland) · 2026Article
- Artificial Intelligence in Psychiatry: Five Decades of Progress and Persistent Translational Challenges.Psychiatric research and clinical practice · 2026Article
- NSX-Net: A Neurolinguistic and Acoustic Multimodal Deep Learning Framework for Speech Disorder Classification.Diagnostics (Basel, Switzerland) · 2026Article
- Computational Analysis of Expressive Behavior in Clinical Assessment.Annual review of clinical psychology · 2026Review
- Help on Demand, a Self-Directed Mobile App Intervention for Gambling Problems: Development and Usability Study.JMIR formative research · 2026Article
- From Tool to Agent: A Semi-Systematic Review of Human-AI Alignment and a Proposed Tiered Healing Ecosystem for Mental Health.Healthcare (Basel, Switzerland) · 2026Review
- Sensors Fusion in Digital Healthcare Applications.Sensors (Basel, Switzerland) · 2026Article
- BactoRamanBioNet: A Multimodal Neural Network for Bacterial Species Identification Using Raman Spectroscopy and Biological Knowledge.Sensors (Basel, Switzerland) · 2026Article
- Low-Burden Detection of Clinical Worsening in Body Dysmorphic Disorder Using Smartphone Sensor and Demographic Data.Behavior therapy · 2026Observational
- LINC: a framework for maintaining high-quality passive data in digital phenotyping studies.Scientific reports · 2026Article
- Generative artificial intelligence and large language models in sports medicine: a scoping review of applications, accuracy, and ethical implications.Frontiers in public health · 2026Article
- Predicting student mental health through entropy-based features and interpretable cross-attention transformer networks.PloS one · 2026Article
- Spatiotemporal multimodal emotion recognition using Temporal video sequences and pose features for child emotion classification.Scientific reports · 2025Article
- [Design and validation of a multimodal model integrating text and imaging data for intelligent assessment of psychological stress in college students].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2025Article
- Detecting Perceived Unfair Treatment Among US College Students Using Mobile Sensing: Pilot Machine Learning Study.JMIR formative research · 2025Article
- POC Sensor Systems and Artificial Intelligence-Where We Are Now and Where We Are Going?Biosensors · 2025Review
- Digital Cardiovascular Twins, AI Agents, and Sensor Data: A Narrative Review from System Architecture to Proactive Heart Health.Sensors (Basel, Switzerland) · 2025Review
- Passive Sensing for Mental Health Monitoring Using Machine Learning With Wearables and Smartphones: Scoping Review.Journal of medical Internet research · 2025Article
- Multimodal Sensing-Enabled Large Language Models for Automated Emotional Regulation: A Review of Current Technologies, Opportunities, and Challenges.Sensors (Basel, Switzerland) · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
As mental health (MH) disorders become increasingly prevalent, their multifaceted symptoms and comorbidities with other conditions introduce complexity to diagnosis, posing a risk of underdiagnosis. While machine learning (ML) has been explored to mitigate these challenges, we hypothesized that multiple data modalities support more comprehensive detection and that non-intrusive collection approaches better capture natural behaviors. To understand the current trends, we systematically reviewed 184 studies to assess feature extraction, feature fusion, and ML methodologies applied to detect MH disorders from passively sensed multimodal data, including audio and video recordings, social media, smartphones, and wearable devices. Our findings revealed varying correlations of modality-specific features in individualized contexts, potentially influenced by demographics and personalities. We also observed the growing adoption of neural network architectures for model-level fusion and as ML algorithms, which have demonstrated promising efficacy in handling high-dimensional features while modeling within and cross-modality relationships. This work provides future researchers with a clear taxonomy of methodological approaches to multimodal detection of MH disorders to inspire future methodological advancements. The comprehensive analysis also guides and supports future researchers in making informed decisions to select an optimal data source that aligns with specific use cases based on the MH disorder of interest.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.